Multiplex Graph Neural Network for Extractive Text Summarization
Baoyu Jing, Zeyu You, Tao Yang, Wei Fan, Hanghang Tong · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.To extract a good summary from a long text document, sentence embedding plays an important role.Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) to learn contextual sentence embedding.However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words).To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words.Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization.Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effectiveness of our method.